data-visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import IPython.display as display\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn\n",
    "# import mpld3\n",
    "from datetime import datetime\n",
    "\n",
    "seaborn.set()\n",
    "np.set_printoptions(precision=4, suppress=True)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "# mpld3.enable_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will generate (and save) images and html source for the ISMIR stats page. Be careful when\n",
    "running the scripts. The save paths have been hard-coded as relative paths and may overwite other files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conference Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Location</th>\n",
       "      <th>Country</th>\n",
       "      <th>Number of Participants</th>\n",
       "      <th>Submitted Papers</th>\n",
       "      <th>Accepted Papers</th>\n",
       "      <th>Acceptance Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Plymouth, Massachusetts</td>\n",
       "      <td>USA</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Bloomington, Indiana</td>\n",
       "      <td>USA</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>Baltimore, Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Spain</td>\n",
       "      <td>189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>London</td>\n",
       "      <td>UK</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>Victoria, British Columbia</td>\n",
       "      <td>Canada</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>Austria</td>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>Philadelphia, Pennsylvania</td>\n",
       "      <td>USA</td>\n",
       "      <td>257</td>\n",
       "      <td>173.0</td>\n",
       "      <td>107</td>\n",
       "      <td>62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>Kobe</td>\n",
       "      <td>Japan</td>\n",
       "      <td>308</td>\n",
       "      <td>202.0</td>\n",
       "      <td>148</td>\n",
       "      <td>73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>245</td>\n",
       "      <td>176.0</td>\n",
       "      <td>110</td>\n",
       "      <td>63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>Miami, Florida</td>\n",
       "      <td>USA</td>\n",
       "      <td>238</td>\n",
       "      <td>223.0</td>\n",
       "      <td>133</td>\n",
       "      <td>60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>Porto</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>226</td>\n",
       "      <td>253.0</td>\n",
       "      <td>100</td>\n",
       "      <td>40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>Curitiba</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>158</td>\n",
       "      <td>252.0</td>\n",
       "      <td>96</td>\n",
       "      <td>38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>213</td>\n",
       "      <td>253.0</td>\n",
       "      <td>106</td>\n",
       "      <td>42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>Málaga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>280</td>\n",
       "      <td>278.0</td>\n",
       "      <td>114</td>\n",
       "      <td>41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>330</td>\n",
       "      <td>238.0</td>\n",
       "      <td>113</td>\n",
       "      <td>47.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>Suzhou</td>\n",
       "      <td>China</td>\n",
       "      <td>279</td>\n",
       "      <td>190.0</td>\n",
       "      <td>97</td>\n",
       "      <td>51.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year                    Location      Country  Number of Participants  \\\n",
       "0   2000  Plymouth, Massachusetts     USA          88                       \n",
       "1   2001  Bloomington, Indiana        USA          100                      \n",
       "2   2002  Paris                       France       175                      \n",
       "3   2003  Baltimore, Maryland         USA          136                      \n",
       "4   2004  Barcelona                   Spain        189                      \n",
       "5   2005  London                      UK           180                      \n",
       "6   2006  Victoria, British Columbia  Canada       180                      \n",
       "7   2007  Vienna                      Austria      250                      \n",
       "8   2008  Philadelphia, Pennsylvania  USA          257                      \n",
       "9   2009  Kobe                        Japan        308                      \n",
       "10  2010  Utrecht                     Netherlands  245                      \n",
       "11  2011  Miami, Florida              USA          238                      \n",
       "12  2012  Porto                       Portugal     226                      \n",
       "13  2013  Curitiba                    Brazil       158                      \n",
       "14  2014  Taipei                      Taiwan       213                      \n",
       "15  2015  Málaga                      Spain        280                      \n",
       "16  2016  New York                    USA          330                      \n",
       "17  2017  Suzhou                      China        279                      \n",
       "\n",
       "    Submitted Papers  Accepted Papers Acceptance Rate  \n",
       "0  NaN                35               NaN             \n",
       "1  NaN                18               NaN             \n",
       "2  NaN                57               NaN             \n",
       "3  NaN                50               NaN             \n",
       "4  NaN                106              NaN             \n",
       "5  NaN                115              NaN             \n",
       "6  NaN                96               NaN             \n",
       "7  NaN                129              NaN             \n",
       "8   173.0             107              62%             \n",
       "9   202.0             148              73%             \n",
       "10  176.0             110              63%             \n",
       "11  223.0             133              60%             \n",
       "12  253.0             100              40%             \n",
       "13  252.0             96               38%             \n",
       "14  253.0             106              42%             \n",
       "15  278.0             114              41%             \n",
       "16  238.0             113              47.5%           \n",
       "17  190.0             97               51.1%           "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/Conference Statistics.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFyCAYAAAAOIVgIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABZGkQMAYGEUOQAAFvb/Hw9NpvvC+jAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b33b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "fields = ['Number of Participants', 'Submitted Papers', 'Accepted Papers']\n",
    "for n, k in enumerate(fields):\n",
    "    ax.bar(data.Year + 0.2*n, data[k], width=0.2, \n",
    "           fc=seaborn.color_palette()[n], label=k)\n",
    "\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_title(\"\")\n",
    "plt.xticks(data.Year + 0.3, [str(v) for v in np.array(data.Year)])\n",
    "plt.legend()\n",
    "# plt.savefig('../ismir_web/img/conference_stats.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Membership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9/8/2002</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6/26/2003</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/11/2004</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/5/2005</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/4/2006</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4/3/2007</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3/27/2008</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2/27/2009</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3/7/2010</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10/2/2011</td>\n",
       "      <td>1505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6/17/2012</td>\n",
       "      <td>1607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4/30/2013</td>\n",
       "      <td>1748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5/20/2014</td>\n",
       "      <td>1825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10/14/2014</td>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4/10/2016</td>\n",
       "      <td>2149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>2252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Members\n",
       "0   9/8/2002    491    \n",
       "1   6/26/2003   608    \n",
       "2   4/11/2004   657    \n",
       "3   3/5/2005    691    \n",
       "4   5/4/2006    812    \n",
       "5   4/3/2007    931    \n",
       "6   3/27/2008   1043   \n",
       "7   2/27/2009   1151   \n",
       "8   3/7/2010    1262   \n",
       "9   10/2/2011   1505   \n",
       "10  6/17/2012   1607   \n",
       "11  4/30/2013   1748   \n",
       "12  5/20/2014   1825   \n",
       "13  10/14/2014  1905   \n",
       "14  4/10/2016   2149   \n",
       "15  11/10/2017  2252   "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/Community Membership.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_format = \"%m/%d/%Y\"\n",
    "d = [datetime.strptime(d, date_format) for d in np.array(data.Date)]\n",
    "d = [dd.days for dd in np.diff(d)]\n",
    "d = np.cumsum(d)\n",
    "d = np.r_[0., d]\n",
    "d = d  / np.max(d) * (len(d) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfkAAAGhCAYAAABia81+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neUD5Py4HHjee8uZhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b33b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "ax.plot(d, data.Members, 'o-', c=seaborn.color_palette()[0], ms=10)\n",
    "plt.xticks(d, data.Date, rotation=45)\n",
    "plt.title(\"Number of Members\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "# plt.savefig('../ismir_web/img/membership_stats.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table for Paper Rankings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "formatter = lambda x: '<strong>'+x+'</strong>' if 'Music Information Retrieval' in x else x\n",
    "data = pd.read_csv(\"data/Google Scholar Publication Rankings - Multimedia.csv\", header=1)\n",
    "cols = data.keys()\n",
    "rank = np.where(['Music Information Retrieval' in r for r in data.Publication])[0]\n",
    "rank = int(data.Rank[rank])\n",
    "if rank == 1:\n",
    "    post = 'st'\n",
    "elif rank == 2:\n",
    "    post = 'nd'\n",
    "elif rank == 3:\n",
    "    post = 'rd'\n",
    "else:\n",
    "    post = 'th'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = ''\n",
    "table += '<p><b>ISMIR</b> is currently the <b>{rank}{post} '.format(rank=rank, post=post)\n",
    "table += 'ranked publication in the &#8220;Multimedia&#8221; subcategory of '\n",
    "table += '&#8220;Engineering and Computer Science&#8221;</b> '\n",
    "table += '<a href=\"https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_multimedia\" '\n",
    "table += 'target=\"new\">(Link.)</a></p>\\n'\n",
    "table += data.to_html(index=False, formatters={'Publication': formatter}, escape=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/Google Scholar Publication Rankings - Music & Musicology.csv\", header=1)\n",
    "cols = data.keys()\n",
    "rank = np.where(['Music Information Retrieval' in r for r in data.Publication])[0]\n",
    "rank = int(data.Rank[rank])\n",
    "if rank == 1:\n",
    "    post = 'st'\n",
    "elif rank == 2:\n",
    "    post = 'nd'\n",
    "elif rank == 3:\n",
    "    post = 'rd'\n",
    "else:\n",
    "    post = 'th'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table += '<p>&nbsp;</p>\\n'\n",
    "table += '<p>and the <b>{rank}{post} '.format(rank=rank, post=post)\n",
    "table += 'ranked in the &#8220;Music &amp; Musicology&#8221; subcategory of '\n",
    "table += '&#8220;Humanities, Literature, and Arts&#8221;</b> '\n",
    "table += '<a href=\"https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=hum_musicmusicology\" '\n",
    "table += 'target=\"new\">(Link.)</a></p>\\n'\n",
    "table += data.to_html(index=False, formatters={'Publication': formatter}, escape=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<p><b>ISMIR</b> is currently the <b>8th ranked publication in the &#8220;Multimedia&#8221; subcategory of &#8220;Engineering and Computer Science&#8221;</b> <a href=\"https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_multimedia\" target=\"new\">(Link.)</a></p>\n",
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th>Rank</th>\n",
      "      <th>Publication</th>\n",
      "      <th>h5-index</th>\n",
      "      <th>h5-median</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <td>1</td>\n",
      "      <td>IEEE Transactions on Multimedia</td>\n",
      "      <td>58</td>\n",
      "      <td>87</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>2</td>\n",
      "      <td>IEEE Transactions on Circuits and Systems for Video Technology</td>\n",
      "      <td>54</td>\n",
      "      <td>83</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>3</td>\n",
      "      <td>ACM International Conference on Multimedia</td>\n",
      "      <td>44</td>\n",
      "      <td>67</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>4</td>\n",
      "      <td>Multimedia Tools and Applications</td>\n",
      "      <td>37</td>\n",
      "      <td>49</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>5</td>\n",
      "      <td>Journal of Visual Communication and Image Representation</td>\n",
      "      <td>36</td>\n",
      "      <td>51</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>6</td>\n",
      "      <td>IEEE International Conference on Image Processing (ICIP)</td>\n",
      "      <td>34</td>\n",
      "      <td>46</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>7</td>\n",
      "      <td>Signal Processing: Image Communication</td>\n",
      "      <td>31</td>\n",
      "      <td>61</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>8</td>\n",
      "      <td><strong>International Society for Music Information Retrieval Conference</strong></td>\n",
      "      <td>29</td>\n",
      "      <td>42</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>9</td>\n",
      "      <td>ACM International Conference on Multimedia Retrieval</td>\n",
      "      <td>29</td>\n",
      "      <td>41</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>10</td>\n",
      "      <td>arXiv Multimedia (cs.MM)</td>\n",
      "      <td>26</td>\n",
      "      <td>38</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table><p>&nbsp;</p>\n",
      "<p>and the <b>2nd ranked in the &#8220;Music &amp; Musicology&#8221; subcategory of &#8220;Humanities, Literature, and Arts&#8221;</b> <a href=\"https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=hum_musicmusicology\" target=\"new\">(Link.)</a></p>\n",
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th>Rank</th>\n",
      "      <th>Publication</th>\n",
      "      <th>h5-index</th>\n",
      "      <th>h5-median</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <td>1</td>\n",
      "      <td>Psychology of Music</td>\n",
      "      <td>31</td>\n",
      "      <td>48</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>2</td>\n",
      "      <td><strong>International Society for Music Information Retrieval Conference</strong></td>\n",
      "      <td>29</td>\n",
      "      <td>42</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>3</td>\n",
      "      <td>Music Perception: An Interdisciplinary Journal</td>\n",
      "      <td>23</td>\n",
      "      <td>34</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>4</td>\n",
      "      <td>Journal of Research in Music Education</td>\n",
      "      <td>19</td>\n",
      "      <td>26</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>5</td>\n",
      "      <td>Music Educators Journal</td>\n",
      "      <td>18</td>\n",
      "      <td>26</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>6</td>\n",
      "      <td>Musicae Scientiae</td>\n",
      "      <td>18</td>\n",
      "      <td>24</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>7</td>\n",
      "      <td>Journal of New Music Research</td>\n",
      "      <td>18</td>\n",
      "      <td>23</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>8</td>\n",
      "      <td>International Journal of Music Education</td>\n",
      "      <td>17</td>\n",
      "      <td>24</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>9</td>\n",
      "      <td>New Interfaces for Musical Expression (NIME)</td>\n",
      "      <td>17</td>\n",
      "      <td>22</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <td>10</td>\n",
      "      <td>Nordic Journal of Music Therapy</td>\n",
      "      <td>16</td>\n",
      "      <td>20</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n"
     ]
    }
   ],
   "source": [
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('stats_source.txt', 'r') as f:\n",
    "    stats_source = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# with open('../ismir_web/stats.php', 'w') as f:\n",
    "#     f.write(stats_source.format(impact=table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}